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Abstract

Data warehouses contain large amounts of information, often collected from a variety of independent sources. Decisionsupport functions in a warehouse, such as on-line analytical processing (OLAP), involve hundreds of complex aggregate queries over large volumes of data. It is not feasible to compute these queries by scanning the data sets each time. Warehouse applications therefore build a large number of summary tables, or materialized aggregate views, to help them increase the system performance. As changes, most notably new transactional data, are collected at the data sources, all summary tables at the warehouse that depend upon this data need to be updated. Usually, source changes are loaded into the warehouse at regular intervals, usually once a day, in a batch window, and the warehouse is made unavailable for querying while it is updated. Since the number of summary tables that need to be maintained is often large, a critical issue for data warehousing is how to maintain the su...

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... group the base data along various dimensions, corresponding to different sets of group-by attributes, and compute various aggregate functions, often called measures. As an example, the cube operator =-=[GBLP96]-=- can be used to define several such summary tables with one statement. As changes are made to the data sources, the warehouse views must be updated to reflect the changed state of the data sources. Th...

...ws four summary tables, each defined as a materialized SQL view. We assume that these views have been chosen to be materialized, either by the database administrator, or by using an algorithm such as =-=[HRU96]-=-. Note that the names of the views have been chosen to reflect the group-by attributes. The character S represents storeID, I represents itemID, and D represents date. The CREATE VIEW SID sales(storeI...

...the exception of the MIN aggregate function in SiC sales. In Section 4.2 we show how the refresh function handles MIN and MAX aggregate functions. 2.3 Previous Aggregation Techniques The technique of =-=[GMS93]-=- works by computing a set of insertions and deletions (combined into one delta set with positive and negative counts) for each materialized view. For aggregate views, whenever a tuple is inserted or...

... tuple is inserted or deleted into a group in the base table, the tuple for the corresponding group is deleted from the materialized view. All such tuples are then recomputed from the base tables. In =-=[GL95]-=-, maintenance expressions for views having duplicate semantics are given, including an example showing how to update an aggregate function (without group by) using the values of the aggregate function...

...ua96] are the only papers that discuss maintenance algorithms for aggregate views. [GMS93, GL95, Qua96] develop algorithms to compute sets of inserted and deleted tuples into an aggregate view, while =-=[JMS95]-=- discusses the computational complexity of immediately maintaining a single aggregate view in response to a single insertion into a chronicle (sequence of tuples). It is worth noting that the previous...

... require that the state of the views not change while the warehouse is being accessed, so that readers see a consistent snapshot of the warehouse across a sequence of multiple queries during analysis =-=[AL80]-=-. For these reasons, warehouses are typically maintained in deferred mode, with the source changes received during the day applied to the views in a nightly batch window, during which time the warehou...

...ively, to decrease the time that the warehouse is unavailable to readers. The paper includes the following contributions: Incremental maintenance techniques for summary tables are given. Except for =-=[Qua96]-=-, previous work on view maintenance has touched upon aggregate views only brie
y [GMS93, GL95]. We propose a new paradigm, called the summary-delta tables method, for maintenance of aggregate views. ...

...ows four summary tables, each dened as a materialized SQL view. We assume that these views have been chosen to be materialized, either by the database administrator, or by using an algorithm such as =-=[HRU96]-=-. CREATE VIEW SID sales(storeID, itemID, date, TotalCount, TotalQuantity) AS SELECT storeID, itemID, date, COUNT(*) AS TotalCount, SUM(qty) AS TotalQuantity FROM pos GROUP BY storeID, itemID, date CRE...

...ning the views v, except that some of the tables in the FROM clause are uniformly replaced by the prepare-changes table. The theorem gives us the desired D-lattice. (A proof of the theorem appears in =-=[Qua97]-=-.) Theorem 5.1 The D-lattice is identical to the V-lattice, including the queries along each edge, modulo a change in the names of tables at each node. Thus, each summary delta table can be derived fr...